基于MaxEnt模型的柳树植原体病害在我国的适生区预测及定量风险分析

doi:10.6048/j.issn.1001-4330.2023.05.023

摘要/Abstract

摘要：

【目的】基于MaxEnt模型的柳树植原体病害在我国的适生区预测及定量风险分析。【方法】利用最大熵模型(MaxEnt),结合ArcGIS软件,预测柳树植原体病害在中国的适生区范围,并通过受试者工作特征(receiver operating characteristic,ROC)验证模型精度,并结合刀切法(jackknife test)筛选主导环境变量。同时建立有害生物风险量化分析体系,计算柳树植原体病的风险性危害值并对其进行评价。【结果】柳树植原体病MaxEnt 模型预测结果的ROC曲线下面积(AUC)平均值为0.979 5,模型预测结果可靠。柳树植原体病害在我国的适生区在22°36'~49°10'N、73°40'~135°05'°E的中温带、暖温带、高原气候区,主要集中在我国的西北、华中、华东、西南、华北和东北地区。其中高度适生区主要集中在西北、华中、华东地区,中度适生区主要集中在东北地区和华中地区中部;低度适生区主要集中在东北和西南地区,非适生区主要集中在华南和港澳台地区。最冷季平均温度(Bio11)、最干月降水量(Bio14)对柳树植原体病在中国的分布状况影响最大,贡献率分别为72.1%和17.4%;等温性(Bio3)、最干季度降水量(Bio17)、最冷季度降水量(Bio19)、最干季度平均温度(Bio9)有一定影响,贡献率依次为3.7%、3.7%、1.9%、1.2%。低温、干旱的区域有利于柳树植原体病的发生。建立5 个准则层、14个指标层的多指标综合评价体系,并对各指标层定量分析,柳树植原体病在我国的风险性危害值(R值)为1.997,属于中度危险林业有害生物。【结论】柳树植原体病风险性较高,需要尽快建立监测体系并针对该病害采取有效控制措施。

关键词: 柳树; 植原体; 病害; 适生分析; 风险评估

Abstract:

【Objective】 To better understand the potential spread and risk of willow phytoplasma in China.【Methods】 In combination with ArcGIS software, the maximum entropy model (MaxEnt) was used to predict the extent of willow phytoplasma in China's adaptive zones, and the model precision was validated by subject working characteristics (ROC), combined with jackknife-test screening for dominant environmental variables. At the same time, a quantitative analysis system of pest risk was set up to calculate the risk value of willow phytoplasma and evaluate it.【Results】The mean area (AUC) under the ROC curve of the MaxEnt model for willow phytoplasma was 0.9795, indicating that the model predicted reliable results. Willow phytoplasma is mainly concentrated in the northwestern, central, eastern, southwestern, northern, and northeastern regions of China in the temperate zones of 22°36'~49°10'N、73°40'~135°05'°E. Among them, the highly suitable areas are mainly in the northwest, central and eastern China, while the moderately suitable areas are mainly in the northeast and central China. The low-conforming areas are mainly in the northeast and southwest, while the non-conforming areas are mainly in southern China, Hong Kong, Macao and Taiwan. Analysis of the significance of environmental variables obtained by the MaxEnt model formalization training gain cutter method showed that the coldest season mean temperature (Bio11) and driest monthly precipitation (Bio14) had the greatest impact on the distribution of willow phycoplasmosis in China, contributing 72.1% and 17.4%, respectively. Isothermia (Bio3), driest quarterly precipitation (Bio17), coldest quarterly precipitation (Bio19), driest quarterly mean temperature (Bio9) had an impact, contributing 3.7%, 3.7%, 1.9%, 1.2%, in order of magnitude. This means that cooler, drier areas are conducive to the development of willow mycoplasmosis. The risk analysis resulted in the creation of a comprehensive multi-indicator evaluation system with 5 guideline levels and 14 indicator levels, and the quantitative analysis of each indicator level showed that willow phytoplasma had a risk value (R value) of 1.997 in our country, which is considered to be a moderately hazardous forest pest.【Conclusion】 The risk of willow phytoplasma is high, so it is necessary to establish a surveillance system and take effective control measures against the disease.

Key words: willow; phytoplasma; disease; fitness analysis; risk assessment

中图分类号:

S432
S76

李静霞, 张学祥, 李丰, 马思洁, 张萍, 朱天生. 基于MaxEnt模型的柳树植原体病害在我国的适生区预测及定量风险分析[J]. 新疆农业科学, 2023, 60(5): 1235-1243.

LI Jingxia, ZHANG Xuexiang, LI Feng, MA Sijie, ZHANG Ping, ZHU Tiansheng. Prediction of suitable areas and quantitative risk analysis of willow phytoplasma diseases in China based on MaxEnt model[J]. Xinjiang Agricultural Sciences, 2023, 60(5): 1235-1243.

图/表 7

参考文献 30

[1]	中国科学院《中国植物志》编辑委员会. 中国植物志:第20卷第2分册[M]. 北京: 科学出版社, 1984.
	Flora of China Editorial Committee of Chinese Academy of Sciences. The Flora of China; Volume 20, Division 2[M]. Beijing: Science Press,1984.
[2]	王宝华. Study on Culyure Value of the Ancient ang Famous Tree in China[D]. 南京: 南京农业大学, 2009.
	WANG Baohua. Study on Culyure Value of the Ancient ang Famous Tree in China[D]. Nanjing: Nanjing Agricultural University, 2009.
[3]	王树凤. 柳树对重金属铅、镉响应的基因型差异及其耐性机制研究[D]. 杭州: 浙江大学, 2015.
	WANG Shufeng. Study on the genotypic variations in Pb/Cd responses and the tolerance mechanisms in Salix integra[D]. Hang zhou: Zhejiang University, 2015.
[4]	O.H F. Witches'-Broom of Willow: Salix Yellows[J]. Phytopathology, 1972, 62(8).
[5]	Verma V S, Singh S. Witches-broom of Indian willow[J]. Zeszyty Problemowe Postepow Nauk Rolniczych, 1981.
[6]	王纯利, 袁自清, 吕巡贤, 等. 新疆柳树丛枝病类菌原体(MLO)的电镜观察[J]. 八一农学院学报, 1992, 15(4):17-19.
	WANG Chunli, YUAN Ziqing, LV Xunxian, et al. Electron microscopical study on mycoplasma-like organism (MLO) on willow witches' broom in Xinjiang[J]. Journal of Xinjiang Agricultural University, 1992, 15(4):17-19.
[7]	Abdul-Hameed KHADHAIR, Chuji HIRUKI. The Molecular Genetic Relatedness of Willow Witches'- Broom Phytoplasma to the Clover Proliferation Group[J]. Proceedings of the Japan Academy, Series B, 1995, 71(5)145-147.
[8]	A Alfaro-Fernández, Abad-Campos P, D Hernández-Llopis, et al. Detection of stolbur phytoplasma in willow in Spain[J]. Bulletin of Insectology, 2011, 64:409-413.
[9]	Zamharir M G, Taheri P. 'Candidatus Phytoplasma solani' related strain associated with Babylon willow witches' broom in central provinces of Iran[J]. Australasian Plant Disease Notes, 2017, 12(1):47. DOI URL
[10]	Shahryari F, Allahverdipour T. "Candidatus Phytoplasma trifolii" related strain affecting Salix babylonica in Iran[J]. Australasian Plant Disease Notes, 2018. 13(1):40. DOI
[11]	朱天生. 四种植原体病害的鉴定[D]. 泰安: 山东农业大学, 2007.
	ZHU Tiansheng. Molecular identification of four phytoplasmal diseases in China[D]. Tai'an: Shandong Agricultural University, 2007.
[12]	笈小龙, 吴育鹏, 谢慧敏, 等. 坡柳(Dodonaea viscosa L.)丛枝植原体新病原的分子鉴定[J]. 基因组学与应用生物学, 2018, 37(6):2423-2429.
	JI Xiaolong, WU Yupeng, XIE Huimin, et al. Molecular Identification of a New Pathogen of Dodonaea viscosa L.Witches' Broom Phytoplasma[J]. Genomics and Applied Biology, 2018, 2018, 37(6):2423-2429.
[13]	魏婷. 柳树黄化病植原体的分子鉴定及相关基因的克隆和生物信息学分析[J]., 西北农林科技大学, 2009, 49(3):389-394. PMID
	WEI Ting. Molecular classification of a phytoplasma associated with willow yellow disease[J]. Acta Microbiologica Sinica, 2009, 49(3):389-394. PMID
[14]	Zhang , ZH, ZH, et al. Detection and Identification of Group 16SrVI Phytoplasma in Willows in China[J]. J PHYTOPATHOL, 2012, 160(11-12):755-757. DOI URL
[15]	Zamhari M G. First report of a ' Candidatus Phytoplasma phoenicium'-related strain (16Sr IX) associated with Salix witches' broom in Iran[J]. New Disease Reports, 2017, 35:37. DOI URL
[16]	赖刚刚. 南疆柳树植原体病的调查及病原鉴定[D]. 阿拉尔: 塔里木大学, 2021.
	LAI Ganggang. Investigation and identification of Willow phytoplasma diseases in Southern Xinjiang[D]. Alar: Tarim University, 2021.
[17]	李岩峰,. 邹大军. 四环素与阿维菌素复配防治柳树植原体病害初探[J]. 林业科技通讯, 2015,(7): 47-48.
	LI Yanfeng, ZOU Dajun. Preliminary study on the control of willow phytoplasma disease with tetracycline and avermectin[J]. Forest Science and Technology, 2015,(7):47-48.
[18]	沈延鑫. 柳树丛枝病的发生及其防治[J]. 农技服务, 2018, 35(1): 88-89.
	SHEN Yanxin. Occurrence and control of willow branch disease[J]. Agricultural Technology Service, 2018, 35(1): 88-89.
[19]	耿站军,. 刘伟. 新疆阿拉尔市柳树丛枝病的发生及防治[J]. 现代园艺, 2020, 43(3):177-178.
	GENG Zhanjun, LIU Wei. Occurrence and control of willow arbuscular disease in Alar City, Xinjiang[J]. Modern Horticulture, 2020, 43(3):177-178.
[20]	刘海秀. 海东市柳树丛枝病的危险性分析[J]. 青海农林科技[J]. 2020,(2):51-53.
	LIU Haixiu. Risk analysis of willow branch disease in Haidong City[J]. Science and Technology of QingHai Agriculture andForestry, 2020,(2):51-53.
[21]	王运生, 谢丙炎, 万方浩, 等. ROC曲线分析在评价入侵物种分布模型中的应用[J]. 生物多样性, 2007,(4):365-372.
	WANG Yunsheng, XIE Binyan, WAN Fanghao, et al. Application of ROC curve analysis in evaluating the performance of alien species’ potential distribution models[J]. Biodiversity Science, 2007,(4):365-372. DOI
[22]	陈新美, 雷渊才, 张雄清, 等. 样本量对MaxEnt模型预测物种分布精度和稳定性的影响[J]. 林业科学, 2012, 48(1):53-59.
	CHEN Xinmei, LEI Yuancai, ZHANG Xunqin, et al. Effects of Sample Sizes on Accuracy and Stability of Maximum Entropy Model in Predicting Species Distribution[J]. Scientia Silvae Sinicae, 2012, 48(1):53-59.
[23]	Swets J. Measuring the accuracy of diagnostic systems[J]. Science, 1988, 240(4857):1285-1293. DOI PMID
[24]	Phillips S J, Anderson R P, Schapire R E. Maximum entropy modeling of species geographic distributions[J]. Ecological modelling, 2006.
[25]	李娟, 赵宇翔, 陈小平, 等. 林业有害生物风险分析指标体系及赋分标准的探讨[J]. 中国森林病虫, 2013, 32(3):10-15.
	LI Juan, ZHAO Yuxiang, CHEN Xiaoping, et al. Discussion on the forest pest risk analysis index system and standard of natural endowments[J]. Forest Pest and Disease, 2013, 32(3):10-15.
[26]	马平, 杜宇, 李正跃, 等. 云南外来入侵有害生物多指标综合评价体系的建立[J]. 植物保护, 2008, 34(3):6.
	MA Ping, DU Yu, LI Zhengyue, et al. Establishment of comprehensive evaluation index system for invasive alien pests linked with plants and plant products in Yunnan[J]. Plant Protection, 2008, 34(3):6.
[27]	蒋青, 梁忆冰, 王乃扬, 等. 有害生物危险性评价的定量分析方法研究[J]. 植物检疫, 1995(4):208-211.
	JIANG Qing, LIANG Yibing, WANG Naiyang, et al. Research on Quantitative Analysis Method of Pest Risk Assessment[J]. Plant Quarantine, 1995(4):208-211.
[28]	王庆璨. 南京几种植原体病害的鉴定及铜钱树丛枝病植原体致病机理的初步研究[D]. 南京: 南京农业大学, 2015.
	WANG Qingcan. Inndentfication of Several Phytoplasmas in Nanjiong and Preliminary Study on Pathogenic Mechanisms of Pal Iurus Hemsleyanus Witches’Broom Phytoplasma[D]. Nanjing Agricultural University, 2015.
[29]	曹学仁, 车海彦, 杨毅, 等. 基于Maxent的椰子致死性黄化植原体在中国的适生性分析[J]. 热带作物学报, 2014, 35(11):2260-2265.
	CAO Xueren, CHE Haiyan, YANG Yi, et al. Potential Distribution of Coconut Lethal Yellowing Phytoplasma in China Predicted Based on Maxent[J]. Chinese Journal of Tropical Crop, 2014, 35(11):2260-2265.
[30]	赖刚刚, 李丰, 党金欢, 等. 新疆南疆柳树植原体病害发生情况调查与分析[J]. 现代园艺, 2021, 44(5):85-87.
	LAI Ganggang, LI Feng, DANG Jinhuan, et al. Investigation and Analysis on the Occurrence of Phytoplasma Diseases of Willow in Southern Xinjiang[J]. Modern Horticulture, 2021, 44(5):85-87.

编辑推荐 0

Metrics

阅读次数

全文

HTML			PDF

最新录用	在线预览	正式出版	最新录用	在线预览	正式出版
0	0	7	0	0	56

来源	本网站	其他网站

次数	45	18
比例	71%	29%

摘要

218

最新录用	在线预览	正式出版

0	0	218

来源	本网站	其他网站

次数	88	130
比例	40%	60%

变量代码 Variable code	描述 Describe	变量代码 Variable code	描述 Describe
Bio1	年均温	Bio11	最冷季度平均温度
Bio2	月均温范围	Bio12	年均降水量
Bio3	等温性	Bio13	最湿月降水量
Bio4	气温的季节性	Bio14	最干月降水量
Bio5	最暖月最高温	Bio15	降水量变异系数
Bio6	最冷月最低温	Bio16	最湿季度降水量
Bio7	气温年范围	Bio17	最干季度降水量
Bio8	最湿季平均温	Bio18	最暖季度降水量
Bio9	最干季度平均温度	Bio19	最冷季度降水量
Bio10	最暖季度平均温度		/

变量代码 Variable code	描述 Describe	变量代码 Variable code	描述 Describe
Bio1	年均温	Bio11	最冷季度平均温度
Bio2	月均温范围	Bio12	年均降水量
Bio3	等温性	Bio13	最湿月降水量
Bio4	气温的季节性	Bio14	最干月降水量
Bio5	最暖月最高温	Bio15	降水量变异系数
Bio6	最冷月最低温	Bio16	最湿季度降水量
Bio7	气温年范围	Bio17	最干季度降水量
Bio8	最湿季平均温	Bio18	最暖季度降水量
Bio9	最干季度平均温度	Bio19	最冷季度降水量
Bio10	最暖季度平均温度		/

序号 Order number	经度 Longitude	纬度 Latitude
1	E49°47'11″	N34°31'48″
2	E50°47'18″	N35°56'28″
3	E51°39'54″	N32°40'14″
4	E54°39'56″	N32°2'26″
5	E74°49'13″	N34°4'29″
6	E75°56'40″	N38°8'43″
7	E76°3'3″	N39°24'17″
8	E76°9'57″	N39°42'54″
9	E76°46'10″	N39°14'8″
10	E77°14'28″	N38°25'1″
11	E77°38'47″	N38°54'2″
12	E79°4'20″	N39°51'59″
13	E79°16'29″	N37°13'14″
14	E79°17'24″	N41°14'58″
15	E79°40'46″	N36°37'15″
16	E79°54'40″	N37°6'45″
17	E80°10'40″	N37°3'58″
18	E80°14'36″	N41°15'46″
19	E81°16'37″	N40°32'31″
20	E81°56'6″	N42°2'10″
21	E82°57'29″	N41°42'42″
22	E84°14'59″	N41°46'32″
23	E86°10'24″	N41°43'25″
24	E87°33'48″	N43°48'48″
25	E98°29'37″	N39°43'55″
26	E101°43'4″	N26°35'9″
27	E101°59'51″	N36°23'59″
28	E101°52'38″	N25°42'15″
29	E102°15'54″	N36°6'3″
30	E102°26'48″	N36°27'59″
31	E102°46'29″	N36°9'22″
32	E112°38'25″	N37°48'12″
33	W0°22'34″	N39°28'10″
34	W71°39'19″	N43°29'5″
35	W72°1'56″	N42°22'43″
36	W74°0'21″	N40°42'45″
37	E109°46'43″	N39°35'58″
38	W113°29'28″	N53°32'46″

序号 Order number	经度 Longitude	纬度 Latitude
1	E49°47'11″	N34°31'48″
2	E50°47'18″	N35°56'28″
3	E51°39'54″	N32°40'14″
4	E54°39'56″	N32°2'26″
5	E74°49'13″	N34°4'29″
6	E75°56'40″	N38°8'43″
7	E76°3'3″	N39°24'17″
8	E76°9'57″	N39°42'54″
9	E76°46'10″	N39°14'8″
10	E77°14'28″	N38°25'1″
11	E77°38'47″	N38°54'2″
12	E79°4'20″	N39°51'59″
13	E79°16'29″	N37°13'14″
14	E79°17'24″	N41°14'58″
15	E79°40'46″	N36°37'15″
16	E79°54'40″	N37°6'45″
17	E80°10'40″	N37°3'58″
18	E80°14'36″	N41°15'46″
19	E81°16'37″	N40°32'31″
20	E81°56'6″	N42°2'10″
21	E82°57'29″	N41°42'42″
22	E84°14'59″	N41°46'32″
23	E86°10'24″	N41°43'25″
24	E87°33'48″	N43°48'48″
25	E98°29'37″	N39°43'55″
26	E101°43'4″	N26°35'9″
27	E101°59'51″	N36°23'59″
28	E101°52'38″	N25°42'15″
29	E102°15'54″	N36°6'3″
30	E102°26'48″	N36°27'59″
31	E102°46'29″	N36°9'22″
32	E112°38'25″	N37°48'12″
33	W0°22'34″	N39°28'10″
34	W71°39'19″	N43°29'5″
35	W72°1'56″	N42°22'43″
36	W74°0'21″	N40°42'45″
37	E109°46'43″	N39°35'58″
38	W113°29'28″	N53°32'46″

准则层(P_i) Criterion layer	指标层(P_ij) Index layer
国内外地理分布(P₁) Geographical distribution both domes-tically and internation-ally	国外分布情况(P₁₁) 国内分布情况(P₁₂)
潜在的危害性(P₂) Potential hazarels	各省级行政区的重视程度(P₂₁) 成为其它检疫性有害生物的传播载体可能性(P₂₂) 生态影响(P₂₃)
寄主植物的重要社会和生态价值(P₃) The lmportant social and ecological values of parasitic plants	受害寄主植物栽培种类(P₃₁) 受害寄主面积(P₃₂)
扩散蔓延可能性(P₄) Potential for diffusion and spread	截获难易程度(P₄₁) 检疫重视程度(P₄₂) 适生范围(P₄₃) 有害生物的传播能力(P₄₄)
危险性管理难度(P₅) Difficulty in risk management	检疫鉴定难度(P₅₁) 防治效果(P₅₂) 根除难度(P₅₃)